Type-Based Unsourced Multiple Access Over Fading Channels in Distributed MIMO With Application to Multi-Target Localization

TL;DR

A performance cost function is proposed that combines localization errors with a misdetection penalty, and is used to characterize how performance depends on the fraction of resources assigned to sensing vs. communication, as well as on the number of bits used to quantize the positions of the targets.

Abstract

We consider the problem of type estimation over unsourced multiple access fading channels in distributed multiple-input multiple-output (D-MIMO) systems. Unlike classical unsourced multiple access, type-based unsourced multiple access (TUMA) aims to estimate the type, i.e., the empirical distribution of transmitted messages. We extend our prior work on TUMA over additive white Gaussian channels to fading scenarios in which neither the transmitters nor the receiver have channel state information. To mitigate the impact of path-loss variability, we employ location-based codebook partitioning: users with similar large-scale fading coefficients use the same codebook. The decoder is built on the multisource approximate message passing algorithm proposed by Cakmak et al. (2025), and supports both centralized and distributed implementations. As an application, we demonstrate how TUMA enables efficient communication in a multi-target localization setting, where distributed sensors report to a D-MIMO receiver quantized target positions. We propose a performance cost function that combines localization errors with a misdetection penalty, and use it to characterize how performance depends on the fraction of resources assigned to sensing vs. communication, as well as on the number of bits used to quantize the positions of the targets.

Figures (9)

• Figure 1: An example of the proposed zone partitioning. In the example, we have $\mathrm{U}\xspace=9$ zones. This partitioning is compatible with the distance-dependent path-loss model described in Section \ref{['SubSec:SimSetup']}.
• Figure 2: Block diagram of the proposed TUMA framework with fading channel in a D-MIMO system.
• Figure 3: Block diagram for message generation process in multi-target localization with TUMA.
• Figure 4: An example of multi-target position localization in a square with $\mathrm{T}\xspace=6$ targets, of which $T_\text{d} = 3$ are detected, $\mathrm{K}\xspace=9$ sensors, of which $K_\mathrm{a} = 7$ are active, and $\mathrm{M}\xspace = 16$ quantized positions. Circles around sensors depict their detection range.
• Figure 5: Empirical distribution of nonzero local multiplicities for the multi-target localization scenario described in Section \ref{['SubSec:SimSetup']}. Here, we consider $\mathrm{T}\xspace=50$ targets, $\mathrm{K}\xspace=200$ sensors, $\log_2\mathrm{M}\xspace=10$ bits, and a sensing blocklength $\mathrm{N}\xspace_\mathrm{s}=1000$.